Project Summary/Abstract Background: Preterm births, those that occur prior to 37 weeks of gestation, are the leading direct cause of neonatal mortality and morbidity. More than 1 in 9 births in the U.S. are preterm, with rates that are disproportionately high among African-Americans and families living in poverty, regardless of race. Addressing the problem of preterm birth requires both accurate identification of the factors that put women at risk, and communication of that risk to women and their healthcare providers. The reduction in preterm births is a core mission of the NICHD Pregnancy and Perinatology Branch. Study aims: 1) Apply our novel machine learning algorithm, KCI-neighbors, to a large prospective cohort study to model adverse pregnancy outcomes among women with low income and English literacy, 2) use mixed-methods research, grounded in decision science techniques, to tailor the MyHealthyPregnancy (MHP) app to the specific needs of low income and low English literacy patients, and 3) conduct small- scale usability testing of the modified technology with a sample of peripartum low-income, low-literacy patients to determine the app's acceptability and interested in targeted intervention strategies. Innovation: MHP is the first mobile health app that combines machine learning, expert models, and behavioral decision research to provide pregnant women and their providers scientifically sound, highly personalized, and actionable feedback on individual-level risk. The machine learning algorithms are designed to learn from users as the app is more widely deployed, allowing for the identification of new causal pathways linking risk factors to adverse pregnancy outcomes. MHP targets specific engagement metrics (e.g. appointment attendance) to meet health system stakeholders' goals, enabling healthcare systems to meet performance targets, as well as decrease costs through reduced adverse outcomes. Methodology and expected results: We will employ statistical machine learning to model the risk of adverse pregnancy outcomes, complemented by qualitative mixed-methods research to identify the most important measures to include in the MHP app. We anticipate that usability-testing will show an engaging app, capable of capturing and communicating risks in our target population. Potential impact: This work will advance scientific understanding of the risk factors, needs, and implementation science required to reach pregnant women who experience the most difficulty engaging in the healthcare system. Moreover, it will help improve clinical practice through development of a tool, MHP, that can detect and communicate preterm birth precursor risk to both patients and providers.